Méthodes d imagerie pour les écoulements et le CND
|
|
- Malcolm Walters
- 6 years ago
- Views:
Transcription
1 Méthodes d imagerie pour les écoulements et le CND Journée scientifique FED3G CEA LIST/Lab Imagerie Tomographie et Traitement Samuel Legoupil 15 juin 2012
2 2D/3D imaging tomography Example Petrochemical reactor scanner : Liquid distribution in a fixed bed reactor (3 hydrodynamics conditions) µtomography imaging : 3D-Fuel injector Ni foam for gas distributor and 3D image analysis results Carbon GDL For fuel cell (fiber=5µm)l Menisci shape in A 100x20 µm canal For microfluidics XPIV for flow characterization 2
3 Outline Context Basics of CT and industrial constraints Projection matrix and modeling needs CS reconstruction methods Implémentation and adaptative data representation Future developments 3
4 Introduction Tomographic Imaging Reconstruction with Non-diffracting Sources Tomographic Imaging with Diffracting Sources Reflection Tomography 4
5 Tomography under progress Number of papers with x-ray and CT, From Ge Wang - Med. Phys. 35, March
6 Differences between medical and industrial imaging Medical Industrial Sample dimensions =0.75 x L=2 m² m Sample dynamics 0 50 Hz 0 10 khz Dose As low as possible No constraint Processing time Short No constraint Contrast 1% 1% Sampling conditions Good Weak most oftenly partial view of object 6
7 Context Growing-up of X-ray CT controls as a standard tool: Material science, biology, Earth science, archeology CT integration for on-line control of manufactured objects turbine blades, medicine industry for spray dispenser High complexity of the method Tedious experimental optimization NIKON 7
8 Outline Context Basics and industrial constraints Projection matrix and modeling needs CS reconstruction methods Implementation and adaptative data representation Future developments 8
9 CT General principles Physics Absorption of media, depends on : Material characteristics (Z density, density, thickness) Photon energy E I I 0 e ( E) d d The optimal energy for the measurement must satisfy : ( E) 2 d 9
10 Absorption coefficient (cm²/g) CT General principles Optimal energy 100 µ - Xcom (cm²/g) ( E) c E d 10 µ - Fit (Power) 5 E 40keV Energy (kev) 10
11 Sample thickness d (cm) Optimal energy (kev) Relative error on measurement CT General principles Optimal energy E d ( E) 2 d R=a/b= E=6keV E=16keV E=11keV Projection angle Projection angle 11
12 Constraints on acquisition Projection configurations 12
13 Constraints on object Object configuration Partial view of the object Complex support of reconstruction domain 13
14 Outline Context Basics and industrial constraints Projection matrix and modeling needs CS reconstruction methods Implementation and adaptative data representation Future developments 14
15 Iterative Algorithms For example, ML-EM, OS-EM, ART, GC, Discretize the image into pixels Solve imaging equations AX=P P X = unknowns (pixel values) P = projection data A = imaging system matrix a ij X 15
16 Iterative Algorithms Regularisation - example Noise model Data matching Prior encouragement V F i 1 2 ( A ) ( 2 i X pi V x i i x j ) If V(x) = x 2, it enforces smoothness. 16
17 Inversion methods Statistical approach Problem description in Emission Tomography Problem description in Transmission Tomography (CT) Estimation of H Law of noise Choice of approach and associated algorithm(s) Iterative method : Statistical inversion : fˆ fˆ arg max x arg max P( g x f ) P( g f ) P( f ) 17
18 Projection matrix in algebraic approaches Reconstruction model I I( E)exp( µ ( x, E) dx) de I I 0 exp( µdx) i Voxel y ln I I 0 µ j A i j Aµ z y x 18
19 Projection matrix in algebraic approaches y j J y Aµ 2 a ij Projection (A): y j a ij i Backprojection (A T ): i j a ij y i j Source 19
20 Photons scattering in cone beam CT Evolution of build-up (1+N scattered photons /N direct photons ) D source-object =50 mm D source-detector =200 mm Det. size=100 mm U=160 kv 20
21 Projection matrix in algebraic approaches y j J y Aµ 2 a ij Projection (A): y j a ij i i a i j Distance to beam axis Backprojection (A T ): i j a ij y j Source 21
22 Projection matrix in algebraic approaches Example of weighted function calculated with CIVA 22
23 Outline Context Basics and industrial constraints Projection matrix and modeling needs CS reconstruction methods Implementation and adaptative data representation Future developments 23
24 Reconstruction from few projections The objective is the reconstruction from few X-ray projections: Speed-up of acquisition process Low-dose for medical imaging The inverse problem is highly ill-posed need for specific approach Original object Frequential domain projections (11 proj.) Reconstruction by analytic inversion 24
25 Reconstruction from few projections Recent developed Compressed sensing theory confirms that with a sampling rate lower than the Nyquist rate, we can also perfectly reconstruct a signal. - Sparse representation of signal under appropriate basis (sinusoid, wavelet ) - Reconstruction solving a convex optimization problem. min x x 1 s. t. Ax b 2 25
26 Reconstruction from few projections As images are rarely sparse, the idea is to find a transform α=(µ) such that the transformed coefficients are sparse, eg Total Variation of µ: TV( ) N 1 N 1 i0 j0 x 2 i, j y 2 i, j min s. t. 1 * A b 2 * Original object Reconstruction by analytical inversion Compressed sensing 26
27 Methods comparison Reconstruction from 32 projections FBP OS-EM Comp. Sensing 27
28 Outline Context Basics and industrial constraints Projection matrix and modeling needs CS reconstruction methods Implementation and adaptative data representation Future developments 28
29 Computer implementation Computer implementation has to estimate g Hf and f H T g Reconstruction Volume = N 3 voxels 2D detector=n² pixels N p projections Matrix H sizes N p xn 2 xn (non-null elements) N N p GBytes GPU hardware 29
30 Example: Ni foam drying ART Bayesian approach Speed-up factors: x 240 on projection x 80 on back projection Reconstruction volumes :
31 Adaptative information representation 32
32 Adaptative information representation 33
33 CIVA NDT Platform CT module 34
34 2D knee reconstruction 35
35 Outline Context Basics and industrial constraints Projection matrix and modeling needs CS reconstruction methods Implementation and adaptative data representation Future developments 36
36 Instantenous imaging Synchronous phenomenon Asynchronous phenomenon max =25% Pompe axis max =39% Relevant information max =44% Multi sources Multi sensors 2 juillet
37 Nouvelles technologies de sources X Avantages des sources à base de CNT : Fort courant (100 µa/tube) Source nano-foyer Très haute résolution spatiale Imagerie par contraste de phase Source étendue Haute intensité Codage de source Sources multiples Tomographie dynamique D. Pribat Voir 2 juillet
38 Simulation of robot CT Local imaging on a «C3» : conformity assessment Defect detection Competition analysis Reconstructed «middle foot 40
39 CIVA-RT / CIVA-CT Platform for NDT evaluation (US, CF and X-ray) GUI Tomo Analytical algo CIVA RX Civa visualisation Plug-in Tomo Algebraic algo Statistical algo High performance calculation 41
40 Future developments Industrial situation Modeling Data Processing Information Sensors Data acquisition Hardware developments X-ray detector: fine resolution, dynamic, multi energy X-ray generator: microfocus source 200 nm), adaptative, c Modeling capacities (from physics to signal theory) and reconstruction Database reconstruction Computation capacities (GPU hardware, Larabi ) Information processing 42
41 43
CIVA Computed Tomography Modeling
CIVA Computed Tomography Modeling R. FERNANDEZ, EXTENDE, France S. LEGOUPIL, M. COSTIN, D. TISSEUR, A. LEVEQUE, CEA-LIST, France page 1 Summary Context From CIVA RT to CIVA CT Reconstruction Methods Applications
More informationAdapted acquisition trajectory and iterative reconstruction for few-views CT inspection
Adapted acquisition trajectory and iterative reconstruction for few-views CT inspection Caroline Vienne 1, Marius Costin 1 More info about this article: http://www.ndt.net/?id=21917 1 CEA, LIST, Département
More informationPhase problem and the Radon transform
Phase problem and the Radon transform Andrei V. Bronnikov Bronnikov Algorithms The Netherlands The Radon transform and applications Inverse problem of phase-contrast CT Fundamental theorem Image reconstruction
More informationLimited view X-ray CT for dimensional analysis
Limited view X-ray CT for dimensional analysis G. A. JONES ( GLENN.JONES@IMPERIAL.AC.UK ) P. HUTHWAITE ( P.HUTHWAITE@IMPERIAL.AC.UK ) NON-DESTRUCTIVE EVALUATION GROUP 1 Outline of talk Industrial X-ray
More informationGPU implementation for rapid iterative image reconstruction algorithm
GPU implementation for rapid iterative image reconstruction algorithm and its applications in nuclear medicine Jakub Pietrzak Krzysztof Kacperski Department of Medical Physics, Maria Skłodowska-Curie Memorial
More informationMEDICAL IMAGE ANALYSIS
SECOND EDITION MEDICAL IMAGE ANALYSIS ATAM P. DHAWAN g, A B IEEE Engineering in Medicine and Biology Society, Sponsor IEEE Press Series in Biomedical Engineering Metin Akay, Series Editor +IEEE IEEE PRESS
More informationAcknowledgments and financial disclosure
AAPM 2012 Annual Meeting Digital breast tomosynthesis: basic understanding of physics principles James T. Dobbins III, Ph.D., FAAPM Director, Medical Physics Graduate Program Ravin Advanced Imaging Laboratories
More informationCharacterization of microshells experimented on Laser Megajoule using X-Ray tomography
Characterization of microshells experimented on Laser Megajoule using X-Ray tomography More info about this article: http://www.ndt.net/?id=20881 Alexandre Choux, Lise Barnouin, Ludovic Reverdy, Marc Theobald
More informationAdvanced Image Reconstruction Methods for Photoacoustic Tomography
Advanced Image Reconstruction Methods for Photoacoustic Tomography Mark A. Anastasio, Kun Wang, and Robert Schoonover Department of Biomedical Engineering Washington University in St. Louis 1 Outline Photoacoustic/thermoacoustic
More informationContinuous and Discrete Image Reconstruction
25 th SSIP Summer School on Image Processing 17 July 2017, Novi Sad, Serbia Continuous and Discrete Image Reconstruction Péter Balázs Department of Image Processing and Computer Graphics University of
More information3/27/2012 WHY SPECT / CT? SPECT / CT Basic Principles. Advantages of SPECT. Advantages of CT. Dr John C. Dickson, Principal Physicist UCLH
3/27/212 Advantages of SPECT SPECT / CT Basic Principles Dr John C. Dickson, Principal Physicist UCLH Institute of Nuclear Medicine, University College London Hospitals and University College London john.dickson@uclh.nhs.uk
More informationAlgebraic Iterative Methods for Computed Tomography
Algebraic Iterative Methods for Computed Tomography Per Christian Hansen DTU Compute Department of Applied Mathematics and Computer Science Technical University of Denmark Per Christian Hansen Algebraic
More informationInvestigation on reconstruction methods applied to 3D terahertz computed Tomography
Investigation on reconstruction methods applied to 3D terahertz computed Tomography B. Recur, 3 A. Younus, 1, P. Mounaix 1, S. Salort, 2 B. Chassagne, 2 P. Desbarats, 3 J-P. Caumes, 2 and E. Abraham 1
More informationLearning Splines for Sparse Tomographic Reconstruction. Elham Sakhaee and Alireza Entezari University of Florida
Learning Splines for Sparse Tomographic Reconstruction Elham Sakhaee and Alireza Entezari University of Florida esakhaee@cise.ufl.edu 2 Tomographic Reconstruction Recover the image given X-ray measurements
More informationCIVA CT, an advanced simulation platform for NDT
More Info at Open Access Database www.ndt.net/?id=18774 CIVA CT, an advanced simulation platform for NDT Marius Costin 1, David Tisseur 1, Caroline Vienne 1, Ronan Guillamet 1, Hussein Banjak 1, Navnina
More informationJoint ICTP-TWAS Workshop on Portable X-ray Analytical Instruments for Cultural Heritage. 29 April - 3 May, 2013
2455-5 Joint ICTP-TWAS Workshop on Portable X-ray Analytical Instruments for Cultural Heritage 29 April - 3 May, 2013 Lecture NoteBasic principles of X-ray Computed Tomography Diego Dreossi Elettra, Trieste
More informationTomography at all Scales. Uccle, 7 April 2014
Tomography at all Scales Uccle, 7 April 2014 Outline The Vision Lab ASTRA: All Scale Tomographic Reconstruction Antwerp Tomography Discrete Tomography In situ CT Superresolution Dynamic imaging The ASTRA
More informationNIH Public Access Author Manuscript Med Phys. Author manuscript; available in PMC 2009 March 13.
NIH Public Access Author Manuscript Published in final edited form as: Med Phys. 2008 February ; 35(2): 660 663. Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic
More informationMedical Image Reconstruction Term II 2012 Topic 6: Tomography
Medical Image Reconstruction Term II 2012 Topic 6: Tomography Professor Yasser Mostafa Kadah Tomography The Greek word tomos means a section, a slice, or a cut. Tomography is the process of imaging a cross
More informationCompressed Sensing for Electron Tomography
University of Maryland, College Park Department of Mathematics February 10, 2015 1/33 Outline I Introduction 1 Introduction 2 3 4 2/33 1 Introduction 2 3 4 3/33 Tomography Introduction Tomography - Producing
More informationTomographic Reconstruction
Tomographic Reconstruction 3D Image Processing Torsten Möller Reading Gonzales + Woods, Chapter 5.11 2 Overview Physics History Reconstruction basic idea Radon transform Fourier-Slice theorem (Parallel-beam)
More informationQuality control phantoms and protocol for a tomography system
Quality control phantoms and protocol for a tomography system Lucía Franco 1 1 CT AIMEN, C/Relva 27A O Porriño Pontevedra, Spain, lfranco@aimen.es Abstract Tomography systems for non-destructive testing
More informationComputed Tomography for Industry Needs and Status Umesh Kumar
Computed Tomography for Industry Needs and Status Umesh Kumar Bhabha Atomic Research Centre, Mumbai, INDIA Industrial Computed Tomography (ICT) Needs Why industrial tomography is required when many conventional
More informationGeneralized Filtered Backprojection for Digital Breast Tomosynthesis Reconstruction
Generalized Filtered Backprojection for Digital Breast Tomosynthesis Reconstruction Klaus Erhard a, Michael Grass a, Sebastian Hitziger b, Armin Iske b and Tim Nielsen a a Philips Research Europe Hamburg,
More informationIntroduction to Positron Emission Tomography
Planar and SPECT Cameras Summary Introduction to Positron Emission Tomography, Ph.D. Nuclear Medicine Basic Science Lectures srbowen@uw.edu System components: Collimator Detector Electronics Collimator
More informationBME I5000: Biomedical Imaging
1 Lucas Parra, CCNY BME I5000: Biomedical Imaging Lecture 4 Computed Tomography Lucas C. Parra, parra@ccny.cuny.edu some slides inspired by lecture notes of Andreas H. Hilscher at Columbia University.
More informationIntroduction to Biomedical Imaging
Alejandro Frangi, PhD Computational Imaging Lab Department of Information & Communication Technology Pompeu Fabra University www.cilab.upf.edu X-ray Projection Imaging Computed Tomography Digital X-ray
More informationBackground. Outline. Radiographic Tomosynthesis: Image Quality and Artifacts Reduction 1 / GE /
Radiographic Tomosynthesis: Image Quality and Artifacts Reduction Baojun Li, Ph.D Department of Radiology Boston University Medical Center 2012 AAPM Annual Meeting Background Linear Trajectory Tomosynthesis
More informationSpiral ASSR Std p = 1.0. Spiral EPBP Std. 256 slices (0/300) Kachelrieß et al., Med. Phys. 31(6): , 2004
Spiral ASSR Std p = 1.0 Spiral EPBP Std p = 1.0 Kachelrieß et al., Med. Phys. 31(6): 1623-1641, 2004 256 slices (0/300) Advantages of Cone-Beam Spiral CT Image quality nearly independent of pitch Increase
More informationCh. 4 Physical Principles of CT
Ch. 4 Physical Principles of CT CLRS 408: Intro to CT Department of Radiation Sciences Review: Why CT? Solution for radiography/tomography limitations Superimposition of structures Distinguishing between
More informationAlgebraic Iterative Methods for Computed Tomography
Algebraic Iterative Methods for Computed Tomography Per Christian Hansen DTU Compute Department of Applied Mathematics and Computer Science Technical University of Denmark Per Christian Hansen Algebraic
More informationMultilevel Optimization for Multi-Modal X-ray Data Analysis
Multilevel Optimization for Multi-Modal X-ray Data Analysis Zichao (Wendy) Di Mathematics & Computer Science Division Argonne National Laboratory May 25, 2016 2 / 35 Outline Multi-Modality Imaging Example:
More informationWorkshop on Quantitative SPECT and PET Brain Studies January, 2013 PUCRS, Porto Alegre, Brasil Corrections in SPECT and PET
Workshop on Quantitative SPECT and PET Brain Studies 14-16 January, 2013 PUCRS, Porto Alegre, Brasil Corrections in SPECT and PET Físico João Alfredo Borges, Me. Corrections in SPECT and PET SPECT and
More informationGPU-Based Acceleration for CT Image Reconstruction
GPU-Based Acceleration for CT Image Reconstruction Xiaodong Yu Advisor: Wu-chun Feng Collaborators: Guohua Cao, Hao Gong Outline Introduction and Motivation Background Knowledge Challenges and Proposed
More informationTemperature Distribution Measurement Based on ML-EM Method Using Enclosed Acoustic CT System
Sensors & Transducers 2013 by IFSA http://www.sensorsportal.com Temperature Distribution Measurement Based on ML-EM Method Using Enclosed Acoustic CT System Shinji Ohyama, Masato Mukouyama Graduate School
More informationInterior Reconstruction Using the Truncated Hilbert Transform via Singular Value Decomposition
Interior Reconstruction Using the Truncated Hilbert Transform via Singular Value Decomposition Hengyong Yu 1, Yangbo Ye 2 and Ge Wang 1 1 CT Laboratory, Biomedical Imaging Division, VT-WFU School of Biomedical
More informationX-ray Tomography. A superficial introduction, but sufficient enough to get us started in surgical navigation.
X-ray Tomography A superficial introduction, but sufficient enough to get us started in surgical navigation. X-ray absorption in homogeneous tissue I o I o / I d m = density I I=I o e -kdm k= constant
More informationDEVELOPMENT OF CONE BEAM TOMOGRAPHIC RECONSTRUCTION SOFTWARE MODULE
Rajesh et al. : Proceedings of the National Seminar & Exhibition on Non-Destructive Evaluation DEVELOPMENT OF CONE BEAM TOMOGRAPHIC RECONSTRUCTION SOFTWARE MODULE Rajesh V Acharya, Umesh Kumar, Gursharan
More informationREDUCED ORDER MODELING IN MULTISPECTRAL PHOTOACOUSTIC TOMOGRAPHY
REDUCED ORDER MODELING IN MULTISPECTRAL PHOTOACOUSTIC TOMOGRAPHY Arvind Saibaba Sarah Vallélian Statistical and Applied Mathematical Sciences Institute & North Carolina State University May 26, 2016 OUTLINE
More informationTomographic reconstruction: the challenge of dark information. S. Roux
Tomographic reconstruction: the challenge of dark information S. Roux Meeting on Tomography and Applications, Politecnico di Milano, 20-22 April, 2015 Tomography A mature technique, providing an outstanding
More informationTotal Variation and Tomographic Imaging from Projections
Total Variation and Tomographic Imaging from Projections Per Christian Hansen & Jakob Heide Jørgensen Technical University of Denmark Joint work with Dr. Emil Sidky & Prof. Xiaochuan Pan Univ. of Chicago
More informationComputer-Tomography II: Image reconstruction and applications
Computer-Tomography II: Image reconstruction and applications Prof. Dr. U. Oelfke DKFZ Heidelberg Department of Medical Physics (E040) Im Neuenheimer Feld 280 69120 Heidelberg, Germany u.oelfke@dkfz.de
More informationMulti-slice CT Image Reconstruction Jiang Hsieh, Ph.D.
Multi-slice CT Image Reconstruction Jiang Hsieh, Ph.D. Applied Science Laboratory, GE Healthcare Technologies 1 Image Generation Reconstruction of images from projections. textbook reconstruction advanced
More informationReconstruction of CT Images from Sparse-View Polyenergetic Data Using Total Variation Minimization
1 Reconstruction of CT Images from Sparse-View Polyenergetic Data Using Total Variation Minimization T. Humphries and A. Faridani Abstract Recent work in CT image reconstruction has seen increasing interest
More informationEnhanced material contrast by dual-energy microct imaging
Enhanced material contrast by dual-energy microct imaging Method note Page 1 of 12 2 Method note: Dual-energy microct analysis 1. Introduction 1.1. The basis for dual energy imaging Micro-computed tomography
More informationIndex. aliasing artifacts and noise in CT images, 200 measurement of projection data, nondiffracting
Index Algebraic equations solution by Kaczmarz method, 278 Algebraic reconstruction techniques, 283-84 sequential, 289, 293 simultaneous, 285-92 Algebraic techniques reconstruction algorithms, 275-96 Algorithms
More informationRecognition and Measurement of Small Defects in ICT Testing
19 th World Conference on Non-Destructive Testing 2016 Recognition and Measurement of Small Defects in ICT Testing Guo ZHIMIN, Ni PEIJUN, Zhang WEIGUO, Qi ZICHENG Inner Mongolia Metallic Materials Research
More informationCoE4TN4 Image Processing. Chapter 5 Image Restoration and Reconstruction
CoE4TN4 Image Processing Chapter 5 Image Restoration and Reconstruction Image Restoration Similar to image enhancement, the ultimate goal of restoration techniques is to improve an image Restoration: a
More informationRadiology. Marta Anguiano Millán. Departamento de Física Atómica, Molecular y Nuclear Facultad de Ciencias. Universidad de Granada
Departamento de Física Atómica, Molecular y Nuclear Facultad de Ciencias. Universidad de Granada Overview Introduction Overview Introduction Tecniques of imaging in Overview Introduction Tecniques of imaging
More informationarxiv: v2 [cond-mat.mtrl-sci] 5 Jan 2010
Gamma scattering scanning of concrete block for detection of voids. Shivaramu 1, Arijit Bose 2 and M. Margret 1 1 Radiological Safety Division, Safety Group, IGCAR, Kalpakaam - 63 12 (India) 2 Chennai
More informationHIGH-SPEED THEE-DIMENSIONAL TOMOGRAPHIC IMAGING OF FRAGMENTS AND PRECISE STATISTICS FROM AN AUTOMATED ANALYSIS
23 RD INTERNATIONAL SYMPOSIUM ON BALLISTICS TARRAGONA, SPAIN 16-20 APRIL 2007 HIGH-SPEED THEE-DIMENSIONAL TOMOGRAPHIC IMAGING OF FRAGMENTS AND PRECISE STATISTICS FROM AN AUTOMATED ANALYSIS P. Helberg 1,
More informationDeformation of granular texture media studied by X-ray CT & 3D DIC at the continuous and microstructure scales
Deformation of granular texture media studied by X-ray CT & 3D DIC at the continuous and microstructure scales N. Lenoir, S.A. Hall, J. Desrues, G. Viggiani and P. Bésuelle (Laboratoire 3S-R) Michel Bornert
More informationPhase-Contrast Imaging and Tomography at 60 kev using a Conventional X-ray Tube
Phase-Contrast Imaging and Tomography at 60 kev using a Conventional X-ray Tube T. Donath* a, F. Pfeiffer a,b, O. Bunk a, W. Groot a, M. Bednarzik a, C. Grünzweig a, E. Hempel c, S. Popescu c, M. Hoheisel
More informationDevelopment of a multi-axis X-ray CT for highly accurate inspection of electronic devices
Development of a multi-axis X-ray CT for highly accurate inspection of electronic devices Toru Kano 1, Michihiko Koseki 2 More info about this article: http://www.ndt.net/?id=20843 1 Tokyo University of
More informationA Projection Access Scheme for Iterative Reconstruction Based on the Golden Section
A Projection Access Scheme for Iterative Reconstruction Based on the Golden Section Thomas Köhler Philips Research Laboratories Roentgenstrasse - Hamburg Germany Abstract A new access scheme for projections
More informationReview of PET Physics. Timothy Turkington, Ph.D. Radiology and Medical Physics Duke University Durham, North Carolina, USA
Review of PET Physics Timothy Turkington, Ph.D. Radiology and Medical Physics Duke University Durham, North Carolina, USA Chart of Nuclides Z (protons) N (number of neutrons) Nuclear Data Evaluation Lab.
More informationLimited View Angle Iterative CT Reconstruction
Limited View Angle Iterative CT Reconstruction Sherman J. Kisner 1, Eri Haneda 1, Charles A. Bouman 1, Sondre Skatter 2, Mikhail Kourinny 2, Simon Bedford 3 1 Purdue University, West Lafayette, IN, USA
More informationIterative CT Reconstruction Using Curvelet-Based Regularization
Iterative CT Reconstruction Using Curvelet-Based Regularization Haibo Wu 1,2, Andreas Maier 1, Joachim Hornegger 1,2 1 Pattern Recognition Lab (LME), Department of Computer Science, 2 Graduate School in
More informationFull-Colour, Computational Ghost Video. Miles Padgett Kelvin Chair of Natural Philosophy
Full-Colour, Computational Ghost Video Miles Padgett Kelvin Chair of Natural Philosophy A Quantum Ghost Imager! Generate random photon pairs, illuminate both object and camera SPDC CCD Identical copies
More informationNON-COLLIMATED SCATTERED RADIATION TOMOGRAPHY
NON-COLLIMATED SCATTERED RADIATION TOMOGRAPHY Gorshkov V.A., Space Research Institute, Moscow, Russia Yumashev V.M., State corporation "Rosatom", Centre "Atom-innovation", Moscow, Russia Kirilenko K.V.,
More informationGE s Revolution CT MATLAB III: CT. Kathleen Chen March 20, 2018
GE s Revolution CT MATLAB III: CT Kathleen Chen chens18@rpi.edu March 20, 2018 https://www.zmescience.com/medicine/inside-human-body-real-time-gifs-demo-power-ct-scan/ Reminders Make sure you have MATLAB
More informationDr. Javier Santillan, San Carlos, CA
X-ray diffraction as a tool for automated residual st ress analysis & a non-synchrotron based nanofocus x-ray computed tomography technique for materials characterization and metrology Dr. Javier Santillan,
More informationAxial block coordinate descent (ABCD) algorithm for X-ray CT image reconstruction
Axial block coordinate descent (ABCD) algorithm for X-ray CT image reconstruction Jeffrey A. Fessler and Donghwan Kim EECS Department University of Michigan Fully 3D Image Reconstruction Conference July
More informationBiomedical Imaging. Computed Tomography. Patrícia Figueiredo IST
Biomedical Imaging Computed Tomography Patrícia Figueiredo IST 2013-2014 Overview Basic principles X ray attenuation projection Slice selection and line projections Projection reconstruction Instrumentation
More informationMoscow-Bavarian Joint Advanced Student School 2006 / Medical Imaging Principles of Computerized Tomographic Imaging and Cone-Beam Reconstruction
Line Integrals Line integrals represent the integral of some parameter of the object along the line (e.g. attenuation of x-rays) Object: f(x,y) Line: x cosθ + y sinθ = t Line integral / Radon transform:
More informationDUE to beam polychromacity in CT and the energy dependence
1 Empirical Water Precorrection for Cone-Beam Computed Tomography Katia Sourbelle, Marc Kachelrieß, Member, IEEE, and Willi A. Kalender Abstract We propose an algorithm to correct for the cupping artifact
More information3D TeraHertz Tomography
3D TeraHertz Tomography B. Recur, 3 A. Younus, 1 S. Salort, 2 P. Mounaix, 1 B. Chassagne, 2 P. Desbarats, 3 1 LOMA, Université de Bordeaux / CNRS 2 ALPhANOV, Centre Technologique Optique et Lasers, Université
More informationA new calibration-free beam hardening reduction method for industrial CT
A new calibration-free beam hardening reduction method for industrial CT ECC 2 for industrial CT Tobias Würfl 1, Nicole Maaß 2, Frank Dennerlein 2, Andreas K. Maier 1 1 Pattern Recognition Lab, FAU Erlangen-Nürnberg;
More informationFinancial disclosure. Onboard imaging modality for IGRT
Tetrahedron Beam Computed Tomography Based On Multi-Pixel X- Ray Source and Its Application in Image Guided Radiotherapy Tiezhi Zhang, Ph.D. Advanced X-ray imaging Lab Financial disclosure Patent royalty
More informationBiophysical Techniques (BPHS 4090/PHYS 5800)
Biophysical Techniques (BPHS 4090/PHYS 5800) Instructors: Prof. Christopher Bergevin (cberge@yorku.ca) Schedule: MWF 1:30-2:30 (CB 122) Website: http://www.yorku.ca/cberge/4090w2017.html York University
More informationSYSTEM LINEARITY LAB MANUAL: 2 Modifications for P551 Fall 2013 Medical Physics Laboratory
SYSTEM LINEARITY LAB MANUAL: 2 Modifications for P551 Fall 2013 Medical Physics Laboratory Introduction In this lab exercise, you will investigate the linearity of the DeskCAT scanner by making measurements
More informationDigital Image Processing
Digital Image Processing SPECIAL TOPICS CT IMAGES Hamid R. Rabiee Fall 2015 What is an image? 2 Are images only about visual concepts? We ve already seen that there are other kinds of image. In this lecture
More informationThe Electrochemical Innovation Lab X-ray Suite: from macro- to nano-ct
The Electrochemical Innovation Lab X-ray Suite: from macro- to nano-ct Dr. Francesco Iacoviello, Toby Neville & the Electrochemical Innovation Lab f.iacoviello@ucl.ac.uk MANIFEST - 2017 November 10 th,
More informationImage Reconstruction from Projection
Image Reconstruction from Projection Reconstruct an image from a series of projections X-ray computed tomography (CT) Computed tomography is a medical imaging method employing tomography where digital
More informationReconstruction from Projections
Reconstruction from Projections M.C. Villa Uriol Computational Imaging Lab email: cruz.villa@upf.edu web: http://www.cilab.upf.edu Based on SPECT reconstruction Martin Šámal Charles University Prague,
More informationDigital Volume Correlation for Materials Characterization
19 th World Conference on Non-Destructive Testing 2016 Digital Volume Correlation for Materials Characterization Enrico QUINTANA, Phillip REU, Edward JIMENEZ, Kyle THOMPSON, Sharlotte KRAMER Sandia National
More informationTotal Variation Regularization Method for 3D Rotational Coronary Angiography
Total Variation Regularization Method for 3D Rotational Coronary Angiography Haibo Wu 1,2, Christopher Rohkohl 1,3, Joachim Hornegger 1,2 1 Pattern Recognition Lab (LME), Department of Computer Science,
More informationFast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach
Fast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach More info about this article: http://www.ndt.net/?id=20852 Eline Janssens 1, Sascha Senck 2, Christoph
More informationImage Acquisition Systems
Image Acquisition Systems Goals and Terminology Conventional Radiography Axial Tomography Computer Axial Tomography (CAT) Magnetic Resonance Imaging (MRI) PET, SPECT Ultrasound Microscopy Imaging ITCS
More informationReconstruction Methods for Coplanar Translational Laminography Applications
Reconstruction Methods for Coplanar Translational Laminography Applications U. EWERT, K.-U. THIESSENHUSEN, A. DERESCH, C. BELLON, S. HOHENDORF, S. KOLKOORI, N. WROBEL, B. REDMER, M. TSCHAIKNER, BAM, Berlin
More informationIntroduction to Emission Tomography
Introduction to Emission Tomography Gamma Camera Planar Imaging Robert Miyaoka, PhD University of Washington Department of Radiology rmiyaoka@u.washington.edu Gamma Camera: - collimator - detector (crystal
More informationEstimating 3D Respiratory Motion from Orbiting Views
Estimating 3D Respiratory Motion from Orbiting Views Rongping Zeng, Jeffrey A. Fessler, James M. Balter The University of Michigan Oct. 2005 Funding provided by NIH Grant P01 CA59827 Motivation Free-breathing
More informationUNIVERSITY OF SOUTHAMPTON
UNIVERSITY OF SOUTHAMPTON PHYS2007W1 SEMESTER 2 EXAMINATION 2014-2015 MEDICAL PHYSICS Duration: 120 MINS (2 hours) This paper contains 10 questions. Answer all questions in Section A and only two questions
More informationCover Page. The handle holds various files of this Leiden University dissertation
Cover Page The handle http://hdl.handle.net/1887/48877 holds various files of this Leiden University dissertation Author: Li, Y. Title: A new method to reconstruct the structure from crystal images Issue
More informationIntroduc)on to PET Image Reconstruc)on. Tomographic Imaging. Projec)on Imaging. Types of imaging systems
Introduc)on to PET Image Reconstruc)on Adam Alessio http://faculty.washington.edu/aalessio/ Nuclear Medicine Lectures Imaging Research Laboratory Division of Nuclear Medicine University of Washington Fall
More informationMULTI-PURPOSE 3D COMPUTED TOMOGRAPHY SYSTEM
MULTI-PURPOSE 3D COMPUTED TOMOGRAPHY SYSTEM M. Simon, C. Sauerwein, I. Tiseanu, S. Burdairon Hans Wälischmiller GmbH Klingleweg 8, D-88709 Meersburg, Germany e-mail: ms@hwm.com ABSTRACT A new flexible
More informationSPECT reconstruction
Regional Training Workshop Advanced Image Processing of SPECT Studies Tygerberg Hospital, 19-23 April 2004 SPECT reconstruction Martin Šámal Charles University Prague, Czech Republic samal@cesnet.cz Tomography
More informationSUPPLEMENTARY INFORMATION
SUPPLEMENTARY INFORMATION doi:10.1038/nature10934 Supplementary Methods Mathematical implementation of the EST method. The EST method begins with padding each projection with zeros (that is, embedding
More informationLABORATORY SYSTEM FOR X-RAY NANOTOMOGRAPHY
79 LABORATORY SYSTEM FOR X-RAY NANOTOMOGRAPHY Alexander Sasov, SkyScan, Vluchtenburgstraat 3, Aartselaar B2630, Belgium, www.skyscan.be. ABSTRACT Using advanced X-ray technologies and X-ray scattering
More informationEmpirical cupping correction: A first-order raw data precorrection for cone-beam computed tomography
Empirical cupping correction: A first-order raw data precorrection for cone-beam computed tomography Marc Kachelrieß, a Katia Sourbelle, and Willi A. Kalender Institute of Medical Physics, University of
More informationMEDICAL IMAGING 2nd Part Computed Tomography
MEDICAL IMAGING 2nd Part Computed Tomography Introduction 2 In the last 30 years X-ray Computed Tomography development produced a great change in the role of diagnostic imaging in medicine. In convetional
More informationA Curvelet based Sinogram Correction Method for Metal Artifact Reduction
A based Sinogram Correction Method for Metal Artifact Reduction Kiwan Jeon 1 and Hyoung Suk Park 1 More info about this article: http://www.ndt.net/?id=3715 1 National Institute for Mathematical Sciences,
More informationSparse Reconstruction / Compressive Sensing
Sparse Reconstruction / Compressive Sensing Namrata Vaswani Department of Electrical and Computer Engineering Iowa State University Namrata Vaswani Sparse Reconstruction / Compressive Sensing 1/ 20 The
More informationExpectation Maximization and Total Variation Based Model for Computed Tomography Reconstruction from Undersampled Data
Expectation Maximization and Total Variation Based Model for Computed Tomography Reconstruction from Undersampled Data Ming Yan and Luminita A. Vese Department of Mathematics, University of California,
More informationCT Systems and their standards
CT Systems and their standards Stephen Brown Engineering Measurement 11 th April 2012 Industrial X-ray computed tomography: The future of co-ordinate metrology? Burleigh Court, Loughborough University
More information8/7/2017. Disclosures. MECT Systems Overview and Quantitative Opportunities. Overview. Computed Tomography (CT) CT Numbers. Polyenergetic Acquisition
Quantitative Multi-Energy Computed Tomography: Imaging and Therapy Advancements Disclosures MECT Systems Overview and Quantitative Opportunities The speaker receives research funding from GE Healthcare
More informationResearch Article SART-Type Image Reconstruction from Overlapped Projections
Hindawi Publishing Corporation International Journal of Biomedical Imaging Volume 2011, Article ID 549537, 7 pages doi:10.1155/2011/549537 Research Article SART-Type Image Reconstruction from Overlapped
More informationCentral Slice Theorem
Central Slice Theorem Incident X-rays y f(x,y) R x r x Detected p(, x ) The thick line is described by xcos +ysin =R Properties of Fourier Transform F [ f ( x a)] F [ f ( x)] e j 2 a Spatial Domain Spatial
More informationImage Reconstruction in Optical Tomography : Utilizing Large Data Sets
Image Reconstruction in Optical Tomography : Utilizing Large Data Sets Vadim A. Markel, Ph.D. Department of Radiology John C. Schotland, M.D., Ph.D. Department of Bioengineering http://whale.seas.upenn.edu
More informationIntroduction to Topics in Machine Learning
Introduction to Topics in Machine Learning Namrata Vaswani Department of Electrical and Computer Engineering Iowa State University Namrata Vaswani 1/ 27 Compressed Sensing / Sparse Recovery: Given y :=
More information